Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations1665
Missing cells1207
Missing cells (%)3.2%
Duplicate rows13
Duplicate rows (%)0.8%
Total size in memory299.3 KiB
Average record size in memory184.1 B

Variable types

Numeric13
Categorical10

Alerts

Dataset has 13 (0.8%) duplicate rowsDuplicates
CycleWithPeakorNot is highly overall correlated with TotalDaysofFertility and 2 other fieldsHigh correlation
EstimatedDayofOvulation is highly overall correlated with FirstDayofHigh and 2 other fieldsHigh correlation
FirstDayofHigh is highly overall correlated with EstimatedDayofOvulationHigh correlation
Group is highly overall correlated with TotalNumberofPeakDaysHigh correlation
LengthofCycle is highly overall correlated with EstimatedDayofOvulation and 1 other fieldsHigh correlation
LengthofMenses is highly overall correlated with TotalMensesScoreHigh correlation
MensesScoreDayFive is highly overall correlated with TotalMensesScoreHigh correlation
MensesScoreDayFour is highly overall correlated with TotalMensesScoreHigh correlation
MensesScoreDayThree is highly overall correlated with TotalMensesScoreHigh correlation
MensesScoreDayTwo is highly overall correlated with TotalMensesScoreHigh correlation
TotalDaysofFertility is highly overall correlated with CycleWithPeakorNot and 1 other fieldsHigh correlation
TotalFertilityFormula is highly overall correlated with EstimatedDayofOvulation and 1 other fieldsHigh correlation
TotalMensesScore is highly overall correlated with LengthofMenses and 4 other fieldsHigh correlation
TotalNumberofHighDays is highly overall correlated with CycleWithPeakorNot and 1 other fieldsHigh correlation
TotalNumberofPeakDays is highly overall correlated with CycleWithPeakorNot and 1 other fieldsHigh correlation
CycleWithPeakorNot is highly imbalanced (57.1%) Imbalance
ReproductiveCategory is highly imbalanced (88.1%) Imbalance
UnusualBleeding is highly imbalanced (67.4%) Imbalance
EstimatedDayofOvulation has 150 (9.0%) missing values Missing
LengthofLutealPhase has 151 (9.1%) missing values Missing
FirstDayofHigh has 258 (15.5%) missing values Missing
TotalDaysofFertility has 31 (1.9%) missing values Missing
MensesScoreDayThree has 25 (1.5%) missing values Missing
MensesScoreDayFour has 87 (5.2%) missing values Missing
MensesScoreDayFive has 434 (26.1%) missing values Missing
UnusualBleeding has 20 (1.2%) missing values Missing
TotalNumberofHighDays has 66 (4.0%) zeros Zeros
TotalHighPostPeak has 1560 (93.7%) zeros Zeros
TotalNumberofPeakDays has 136 (8.2%) zeros Zeros
TotalDaysofFertility has 30 (1.8%) zeros Zeros
NumberofDaysofIntercourse has 157 (9.4%) zeros Zeros

Reproduction

Analysis started2024-12-11 15:50:23.054826
Analysis finished2024-12-11 15:51:45.169324
Duration1 minute and 22.11 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

CycleNumber
Real number (ℝ)

Distinct45
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0408408
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:45.508492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q311
95-th percentile21.8
Maximum45
Range44
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.5936861
Coefficient of variation (CV)0.82002445
Kurtosis4.8477577
Mean8.0408408
Median Absolute Deviation (MAD)4
Skewness1.8512591
Sum13388
Variance43.476696
MonotonicityNot monotonic
2024-12-11T15:51:46.108980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 163
 
9.8%
2 152
 
9.1%
3 137
 
8.2%
4 127
 
7.6%
5 119
 
7.1%
6 112
 
6.7%
7 107
 
6.4%
8 104
 
6.2%
9 100
 
6.0%
10 99
 
5.9%
Other values (35) 445
26.7%
ValueCountFrequency (%)
1 163
9.8%
2 152
9.1%
3 137
8.2%
4 127
7.6%
5 119
7.1%
6 112
6.7%
7 107
6.4%
8 104
6.2%
9 100
6.0%
10 99
5.9%
ValueCountFrequency (%)
45 1
0.1%
44 1
0.1%
43 1
0.1%
42 1
0.1%
41 1
0.1%
40 1
0.1%
39 1
0.1%
38 1
0.1%
37 1
0.1%
36 1
0.1%

Group
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
0
1028 
1
637 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1665
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

Length

2024-12-11T15:51:46.707427image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:47.158076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

Most occurring characters

ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1028
61.7%
1 637
38.3%

CycleWithPeakorNot
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
1
1519 
0
 
146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1665
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

Length

2024-12-11T15:51:47.708536image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:48.177938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

Most occurring characters

ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1519
91.2%
0 146
 
8.8%

ReproductiveCategory
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
0
1609 
1
 
48
2
 
4
9
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1665
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

Length

2024-12-11T15:51:48.577160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:48.841684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1609
96.6%
1 48
 
2.9%
2 4
 
0.2%
9 4
 
0.2%

LengthofCycle
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.299099
Minimum18
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:49.115530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q127
median29
Q331
95-th percentile37
Maximum54
Range36
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8879319
Coefficient of variation (CV)0.132698
Kurtosis3.2562926
Mean29.299099
Median Absolute Deviation (MAD)2
Skewness1.2894862
Sum48783
Variance15.116015
MonotonicityNot monotonic
2024-12-11T15:51:49.468875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
28 236
14.2%
27 214
12.9%
29 190
11.4%
26 185
11.1%
30 151
9.1%
31 113
6.8%
25 111
6.7%
32 109
6.5%
33 74
 
4.4%
24 52
 
3.1%
Other values (22) 230
13.8%
ValueCountFrequency (%)
18 2
 
0.1%
19 1
 
0.1%
20 1
 
0.1%
21 4
 
0.2%
22 2
 
0.1%
23 19
 
1.1%
24 52
 
3.1%
25 111
6.7%
26 185
11.1%
27 214
12.9%
ValueCountFrequency (%)
54 1
 
0.1%
51 1
 
0.1%
49 1
 
0.1%
48 2
 
0.1%
45 2
 
0.1%
44 1
 
0.1%
43 3
 
0.2%
42 8
0.5%
41 4
 
0.2%
40 14
0.8%

EstimatedDayofOvulation
Real number (ℝ)

High correlation  Missing 

Distinct23
Distinct (%)1.5%
Missing150
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean15.963036
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:49.773309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q318
95-th percentile23
Maximum29
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5484875
Coefficient of variation (CV)0.22229402
Kurtosis1.1013119
Mean15.963036
Median Absolute Deviation (MAD)2
Skewness0.92639426
Sum24184
Variance12.591764
MonotonicityNot monotonic
2024-12-11T15:51:50.072116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
14 227
13.6%
15 205
12.3%
13 171
10.3%
16 170
10.2%
18 123
7.4%
17 110
6.6%
12 109
6.5%
19 94
5.6%
20 61
 
3.7%
11 55
 
3.3%
Other values (13) 190
11.4%
(Missing) 150
9.0%
ValueCountFrequency (%)
6 1
 
0.1%
8 3
 
0.2%
9 9
 
0.5%
10 20
 
1.2%
11 55
 
3.3%
12 109
6.5%
13 171
10.3%
14 227
13.6%
15 205
12.3%
16 170
10.2%
ValueCountFrequency (%)
29 6
 
0.4%
28 6
 
0.4%
27 7
 
0.4%
26 11
 
0.7%
25 13
 
0.8%
24 20
 
1.2%
23 26
1.6%
22 26
1.6%
21 42
2.5%
20 61
3.7%

LengthofLutealPhase
Real number (ℝ)

Missing 

Distinct29
Distinct (%)1.9%
Missing151
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean13.270806
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:50.378076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q112
median13
Q314
95-th percentile17
Maximum41
Range40
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6716016
Coefficient of variation (CV)0.2013142
Kurtosis14.860795
Mean13.270806
Median Absolute Deviation (MAD)1
Skewness1.9781273
Sum20092
Variance7.1374551
MonotonicityNot monotonic
2024-12-11T15:51:50.703591image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
13 338
20.3%
12 288
17.3%
14 267
16.0%
15 186
11.2%
11 126
 
7.6%
10 77
 
4.6%
16 59
 
3.5%
9 40
 
2.4%
17 35
 
2.1%
18 23
 
1.4%
Other values (19) 75
 
4.5%
(Missing) 151
9.1%
ValueCountFrequency (%)
1 1
 
0.1%
4 4
 
0.2%
5 2
 
0.1%
6 2
 
0.1%
7 1
 
0.1%
8 17
 
1.0%
9 40
 
2.4%
10 77
 
4.6%
11 126
7.6%
12 288
17.3%
ValueCountFrequency (%)
41 1
 
0.1%
34 1
 
0.1%
32 1
 
0.1%
29 1
 
0.1%
28 1
 
0.1%
27 1
 
0.1%
26 2
 
0.1%
24 2
 
0.1%
23 4
0.2%
22 6
0.4%

FirstDayofHigh
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)1.6%
Missing258
Missing (%)15.5%
Infinite0
Infinite (%)0.0%
Mean11.761905
Minimum5
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:51.012953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q110
median11
Q313
95-th percentile18
Maximum26
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2537579
Coefficient of variation (CV)0.27663529
Kurtosis1.4567635
Mean11.761905
Median Absolute Deviation (MAD)2
Skewness0.92354226
Sum16549
Variance10.58694
MonotonicityNot monotonic
2024-12-11T15:51:51.323468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10 207
12.4%
12 195
11.7%
11 191
11.5%
9 149
8.9%
13 135
8.1%
14 104
6.2%
8 94
 
5.6%
15 67
 
4.0%
7 61
 
3.7%
16 53
 
3.2%
Other values (12) 151
9.1%
(Missing) 258
15.5%
ValueCountFrequency (%)
5 5
 
0.3%
6 26
 
1.6%
7 61
 
3.7%
8 94
5.6%
9 149
8.9%
10 207
12.4%
11 191
11.5%
12 195
11.7%
13 135
8.1%
14 104
6.2%
ValueCountFrequency (%)
26 2
 
0.1%
25 2
 
0.1%
24 4
 
0.2%
23 4
 
0.2%
22 5
 
0.3%
21 7
 
0.4%
20 13
 
0.8%
19 12
 
0.7%
18 31
1.9%
17 40
2.4%

TotalNumberofHighDays
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)1.4%
Missing12
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean4.2480339
Minimum0
Maximum22
Zeros66
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:51.605029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum22
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.502879
Coefficient of variation (CV)0.82458829
Kurtosis4.2055693
Mean4.2480339
Median Absolute Deviation (MAD)2
Skewness1.8530225
Sum7022
Variance12.270161
MonotonicityNot monotonic
2024-12-11T15:51:51.892095image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 275
16.5%
3 268
16.1%
1 236
14.2%
4 231
13.9%
5 180
10.8%
6 122
7.3%
7 85
 
5.1%
0 66
 
4.0%
8 32
 
1.9%
10 32
 
1.9%
Other values (13) 126
7.6%
ValueCountFrequency (%)
0 66
 
4.0%
1 236
14.2%
2 275
16.5%
3 268
16.1%
4 231
13.9%
5 180
10.8%
6 122
7.3%
7 85
 
5.1%
8 32
 
1.9%
9 21
 
1.3%
ValueCountFrequency (%)
22 1
 
0.1%
21 2
 
0.1%
20 1
 
0.1%
19 5
 
0.3%
18 8
0.5%
17 7
0.4%
16 11
0.7%
15 10
0.6%
14 14
0.8%
13 15
0.9%

TotalHighPostPeak
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.5%
Missing3
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.13357401
Minimum0
Maximum7
Zeros1560
Zeros (%)93.7%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:52.142054image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61309539
Coefficient of variation (CV)4.5899303
Kurtosis41.654783
Mean0.13357401
Median Absolute Deviation (MAD)0
Skewness5.9108331
Sum222
Variance0.37588595
MonotonicityNot monotonic
2024-12-11T15:51:52.446162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1560
93.7%
2 41
 
2.5%
1 34
 
2.0%
3 15
 
0.9%
5 4
 
0.2%
4 4
 
0.2%
6 3
 
0.2%
7 1
 
0.1%
(Missing) 3
 
0.2%
ValueCountFrequency (%)
0 1560
93.7%
1 34
 
2.0%
2 41
 
2.5%
3 15
 
0.9%
4 4
 
0.2%
5 4
 
0.2%
6 3
 
0.2%
7 1
 
0.1%
ValueCountFrequency (%)
7 1
 
0.1%
6 3
 
0.2%
5 4
 
0.2%
4 4
 
0.2%
3 15
 
0.9%
2 41
 
2.5%
1 34
 
2.0%
0 1560
93.7%

TotalNumberofPeakDays
Real number (ℝ)

High correlation  Zeros 

Distinct14
Distinct (%)0.8%
Missing16
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1.9211643
Minimum0
Maximum13
Zeros136
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:52.735210image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1940771
Coefficient of variation (CV)0.62153821
Kurtosis16.943743
Mean1.9211643
Median Absolute Deviation (MAD)0
Skewness2.7985274
Sum3168
Variance1.42582
MonotonicityNot monotonic
2024-12-11T15:51:53.019045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 1062
63.8%
1 270
 
16.2%
0 136
 
8.2%
3 84
 
5.0%
4 46
 
2.8%
6 17
 
1.0%
5 16
 
1.0%
7 8
 
0.5%
9 4
 
0.2%
10 2
 
0.1%
Other values (4) 4
 
0.2%
(Missing) 16
 
1.0%
ValueCountFrequency (%)
0 136
 
8.2%
1 270
 
16.2%
2 1062
63.8%
3 84
 
5.0%
4 46
 
2.8%
5 16
 
1.0%
6 17
 
1.0%
7 8
 
0.5%
8 1
 
0.1%
9 4
 
0.2%
ValueCountFrequency (%)
13 1
 
0.1%
12 1
 
0.1%
11 1
 
0.1%
10 2
 
0.1%
9 4
 
0.2%
8 1
 
0.1%
7 8
 
0.5%
6 17
 
1.0%
5 16
 
1.0%
4 46
2.8%

TotalDaysofFertility
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct25
Distinct (%)1.5%
Missing31
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean7.995716
Minimum0
Maximum27
Zeros30
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:53.310348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q16
median7
Q310
95-th percentile14
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2725706
Coefficient of variation (CV)0.4092905
Kurtosis2.5743806
Mean7.995716
Median Absolute Deviation (MAD)2
Skewness1.0433479
Sum13065
Variance10.709718
MonotonicityNot monotonic
2024-12-11T15:51:53.624840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
7 270
16.2%
5 262
15.7%
6 246
14.8%
8 206
12.4%
9 170
10.2%
10 141
8.5%
11 76
 
4.6%
12 45
 
2.7%
13 38
 
2.3%
14 30
 
1.8%
Other values (15) 150
9.0%
(Missing) 31
 
1.9%
ValueCountFrequency (%)
0 30
 
1.8%
1 3
 
0.2%
2 4
 
0.2%
3 5
 
0.3%
4 27
 
1.6%
5 262
15.7%
6 246
14.8%
7 270
16.2%
8 206
12.4%
9 170
10.2%
ValueCountFrequency (%)
27 1
 
0.1%
24 1
 
0.1%
22 1
 
0.1%
21 2
 
0.1%
20 4
 
0.2%
19 5
 
0.3%
18 12
0.7%
17 12
0.7%
16 22
1.3%
15 21
1.3%

TotalFertilityFormula
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)1.7%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14.285628
Minimum6
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:53.907057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q112
median13
Q316
95-th percentile23
Maximum37
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9756575
Coefficient of variation (CV)0.2782977
Kurtosis3.0128412
Mean14.285628
Median Absolute Deviation (MAD)2
Skewness1.459277
Sum23757
Variance15.805852
MonotonicityNot monotonic
2024-12-11T15:51:54.205613image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
12 256
15.4%
13 225
13.5%
11 211
12.7%
14 184
11.1%
15 134
8.0%
16 118
7.1%
10 104
6.2%
17 101
 
6.1%
18 62
 
3.7%
9 48
 
2.9%
Other values (19) 220
13.2%
ValueCountFrequency (%)
6 2
 
0.1%
7 3
 
0.2%
8 17
 
1.0%
9 48
 
2.9%
10 104
6.2%
11 211
12.7%
12 256
15.4%
13 225
13.5%
14 184
11.1%
15 134
8.0%
ValueCountFrequency (%)
37 1
 
0.1%
36 1
 
0.1%
35 1
 
0.1%
33 1
 
0.1%
31 1
 
0.1%
29 2
 
0.1%
28 7
0.4%
27 11
0.7%
26 9
0.5%
25 15
0.9%

LengthofMenses
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)0.7%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5.2372065
Minimum2
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:54.512679image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum15
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2559594
Coefficient of variation (CV)0.23981476
Kurtosis3.0606886
Mean5.2372065
Median Absolute Deviation (MAD)1
Skewness0.73685183
Sum8699
Variance1.5774341
MonotonicityNot monotonic
2024-12-11T15:51:54.794778image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 629
37.8%
6 380
22.8%
4 346
20.8%
7 155
 
9.3%
3 63
 
3.8%
8 41
 
2.5%
2 21
 
1.3%
9 20
 
1.2%
10 4
 
0.2%
11 1
 
0.1%
(Missing) 4
 
0.2%
ValueCountFrequency (%)
2 21
 
1.3%
3 63
 
3.8%
4 346
20.8%
5 629
37.8%
6 380
22.8%
7 155
 
9.3%
8 41
 
2.5%
9 20
 
1.2%
10 4
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
15 1
 
0.1%
11 1
 
0.1%
10 4
 
0.2%
9 20
 
1.2%
8 41
 
2.5%
7 155
 
9.3%
6 380
22.8%
5 629
37.8%
4 346
20.8%
3 63
 
3.8%
Distinct3
Distinct (%)0.2%
Missing4
Missing (%)0.2%
Memory size13.1 KiB
3
814 
2
514 
1
333 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 814
48.9%
2 514
30.9%
1 333
20.0%
(Missing) 4
 
0.2%

Length

2024-12-11T15:51:55.088417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:55.343720image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 814
49.0%
2 514
30.9%
1 333
20.0%

Most occurring characters

ValueCountFrequency (%)
3 814
49.0%
2 514
30.9%
1 333
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 814
49.0%
2 514
30.9%
1 333
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 814
49.0%
2 514
30.9%
1 333
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 814
49.0%
2 514
30.9%
1 333
20.0%

MensesScoreDayTwo
Categorical

High correlation 

Distinct3
Distinct (%)0.2%
Missing4
Missing (%)0.2%
Memory size13.1 KiB
3
1002 
2
566 
1
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1661
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1002
60.2%
2 566
34.0%
1 93
 
5.6%
(Missing) 4
 
0.2%

Length

2024-12-11T15:51:55.657143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:55.911902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 1002
60.3%
2 566
34.1%
1 93
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 1002
60.3%
2 566
34.1%
1 93
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1002
60.3%
2 566
34.1%
1 93
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1002
60.3%
2 566
34.1%
1 93
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1661
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1002
60.3%
2 566
34.1%
1 93
 
5.6%

MensesScoreDayThree
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing25
Missing (%)1.5%
Memory size13.1 KiB
2
865 
3
420 
1
355 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1640
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 865
52.0%
3 420
25.2%
1 355
21.3%
(Missing) 25
 
1.5%

Length

2024-12-11T15:51:56.179990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:56.464969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 865
52.7%
3 420
25.6%
1 355
21.6%

Most occurring characters

ValueCountFrequency (%)
2 865
52.7%
3 420
25.6%
1 355
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 865
52.7%
3 420
25.6%
1 355
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 865
52.7%
3 420
25.6%
1 355
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 865
52.7%
3 420
25.6%
1 355
21.6%

MensesScoreDayFour
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing87
Missing (%)5.2%
Memory size13.1 KiB
1
891 
2
550 
3
137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1578
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 891
53.5%
2 550
33.0%
3 137
 
8.2%
(Missing) 87
 
5.2%

Length

2024-12-11T15:51:56.741799image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:56.986690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 891
56.5%
2 550
34.9%
3 137
 
8.7%

Most occurring characters

ValueCountFrequency (%)
1 891
56.5%
2 550
34.9%
3 137
 
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1578
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 891
56.5%
2 550
34.9%
3 137
 
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1578
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 891
56.5%
2 550
34.9%
3 137
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1578
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 891
56.5%
2 550
34.9%
3 137
 
8.7%

MensesScoreDayFive
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.2%
Missing434
Missing (%)26.1%
Memory size13.1 KiB
1
979 
2
207 
3
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1231
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 979
58.8%
2 207
 
12.4%
3 45
 
2.7%
(Missing) 434
26.1%

Length

2024-12-11T15:51:58.064152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:51:58.306506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 979
79.5%
2 207
 
16.8%
3 45
 
3.7%

Most occurring characters

ValueCountFrequency (%)
1 979
79.5%
2 207
 
16.8%
3 45
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1231
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 979
79.5%
2 207
 
16.8%
3 45
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1231
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 979
79.5%
2 207
 
16.8%
3 45
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1231
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 979
79.5%
2 207
 
16.8%
3 45
 
3.7%

TotalMensesScore
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)1.3%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean9.8500903
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:58.665741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q18
median10
Q311
95-th percentile14
Maximum24
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7865504
Coefficient of variation (CV)0.28289592
Kurtosis1.9099826
Mean9.8500903
Median Absolute Deviation (MAD)2
Skewness0.66658697
Sum16361
Variance7.7648629
MonotonicityNot monotonic
2024-12-11T15:51:59.173344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9 269
16.2%
10 265
15.9%
8 222
13.3%
11 217
13.0%
12 153
9.2%
7 140
8.4%
6 93
 
5.6%
13 88
 
5.3%
14 72
 
4.3%
5 38
 
2.3%
Other values (12) 104
 
6.2%
ValueCountFrequency (%)
2 5
 
0.3%
3 12
 
0.7%
4 8
 
0.5%
5 38
 
2.3%
6 93
 
5.6%
7 140
8.4%
8 222
13.3%
9 269
16.2%
10 265
15.9%
11 217
13.0%
ValueCountFrequency (%)
24 2
 
0.1%
23 1
 
0.1%
22 3
 
0.2%
20 3
 
0.2%
19 3
 
0.2%
18 8
 
0.5%
17 15
 
0.9%
16 20
 
1.2%
15 24
 
1.4%
14 72
4.3%

NumberofDaysofIntercourse
Real number (ℝ)

Zeros 

Distinct20
Distinct (%)1.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.1544471
Minimum0
Maximum20
Zeros157
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size13.1 KiB
2024-12-11T15:51:59.708768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.054851
Coefficient of variation (CV)0.73532072
Kurtosis2.2272641
Mean4.1544471
Median Absolute Deviation (MAD)2
Skewness1.131186
Sum6913
Variance9.3321149
MonotonicityNot monotonic
2024-12-11T15:52:00.283891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 240
14.4%
4 234
14.1%
2 228
13.7%
5 184
11.1%
1 158
9.5%
0 157
9.4%
6 133
8.0%
7 121
7.3%
8 72
 
4.3%
9 54
 
3.2%
Other values (10) 83
 
5.0%
ValueCountFrequency (%)
0 157
9.4%
1 158
9.5%
2 228
13.7%
3 240
14.4%
4 234
14.1%
5 184
11.1%
6 133
8.0%
7 121
7.3%
8 72
 
4.3%
9 54
 
3.2%
ValueCountFrequency (%)
20 1
 
0.1%
19 3
 
0.2%
18 2
 
0.1%
16 3
 
0.2%
15 5
 
0.3%
14 5
 
0.3%
13 12
0.7%
12 10
 
0.6%
11 14
0.8%
10 28
1.7%
Distinct2
Distinct (%)0.1%
Missing1
Missing (%)0.1%
Memory size13.1 KiB
0
1028 
1
636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1664
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 1028
61.7%
1 636
38.2%
(Missing) 1
 
0.1%

Length

2024-12-11T15:52:00.795180image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:52:01.127254image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1028
61.8%
1 636
38.2%

Most occurring characters

ValueCountFrequency (%)
0 1028
61.8%
1 636
38.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1664
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1028
61.8%
1 636
38.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1664
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1028
61.8%
1 636
38.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1664
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1028
61.8%
1 636
38.2%

UnusualBleeding
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing20
Missing (%)1.2%
Memory size13.1 KiB
0
1547 
1
 
98

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1645
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1547
92.9%
1 98
 
5.9%
(Missing) 20
 
1.2%

Length

2024-12-11T15:52:01.611297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-11T15:52:02.076143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 1547
94.0%
1 98
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 1547
94.0%
1 98
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1547
94.0%
1 98
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1547
94.0%
1 98
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1547
94.0%
1 98
 
6.0%

Interactions

2024-12-11T15:51:36.263218image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:41.488973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:45.380500image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:49.949217image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:54.032201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:03.630306image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:13.186970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:16.709061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:21.695999image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:25.581747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:30.584429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:36.696702image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:28.042755image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:33.724581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:42.035442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:54.398431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:07.975257image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:13.445665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:17.164029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:21.983109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:25.942698image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:30.932079image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:34.502825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:08.947201image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:13.974605image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:18.044694image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:26.759745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:31.898235image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:39.349885image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:29.777594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:36.388667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:09.653212image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:14.289896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:18.583398image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:22.909656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:32.435648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:43.316025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:19.029556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:23.195884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:32.862047image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:43.600076image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:50:31.423916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:41.752345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:50:31.798899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:21.412035image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-12-11T15:51:30.189734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-12-11T15:51:35.809355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-12-11T15:52:02.453404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
CycleNumberCycleWithPeakorNotEstimatedDayofOvulationFirstDayofHighGroupIntercourseInFertileWindowLengthofCycleLengthofLutealPhaseLengthofMensesMensesScoreDayFiveMensesScoreDayFourMensesScoreDayOneMensesScoreDayThreeMensesScoreDayTwoNumberofDaysofIntercourseReproductiveCategoryTotalDaysofFertilityTotalFertilityFormulaTotalHighPostPeakTotalMensesScoreTotalNumberofHighDaysTotalNumberofPeakDaysUnusualBleeding
CycleNumber1.0000.029-0.136-0.0550.1530.179-0.1010.094-0.1820.0060.0870.0870.0680.050-0.0200.139-0.070-0.179-0.093-0.140-0.063-0.0220.000
CycleWithPeakorNot0.0291.0000.0000.1120.1130.0830.1340.2580.0570.0120.0400.0590.0190.0210.0710.0000.6180.4420.0420.0880.6220.9560.000
EstimatedDayofOvulation-0.1360.0001.0000.6490.1890.1610.728-0.3170.2270.0680.0990.0510.0360.0890.1010.1980.3620.838-0.0630.1700.2000.2440.213
FirstDayofHigh-0.0550.1120.6491.0000.1830.1520.470-0.2030.1370.0000.0000.0560.0210.0810.0280.189-0.2840.470-0.1260.098-0.282-0.0200.237
Group0.1530.1130.1890.1831.0000.0000.0360.2520.3630.1860.2310.2180.2150.1200.1190.1190.2400.0980.2410.2820.2170.7440.023
IntercourseInFertileWindow0.1790.0830.1610.1520.0001.0000.1450.1270.0390.0600.0130.0540.0000.0560.3200.1550.1120.2350.0420.0980.0940.1130.000
LengthofCycle-0.1010.1340.7280.4700.0360.1451.0000.3270.1720.0720.0380.0930.0000.0830.1320.1800.2180.5970.0130.1440.1500.0770.182
LengthofLutealPhase0.0940.258-0.317-0.2030.2520.1270.3271.000-0.0810.1780.1960.1930.1450.1120.0160.301-0.190-0.2470.077-0.044-0.058-0.1530.147
LengthofMenses-0.1820.0570.2270.1370.3630.0390.172-0.0811.0000.4730.4580.1770.3570.3530.0550.3000.1350.1800.0900.7990.0530.0340.404
MensesScoreDayFive0.0060.0120.0680.0000.1860.0600.0720.1780.4731.0000.4610.1400.3010.1630.0480.0660.1530.0880.0570.6510.1060.2200.256
MensesScoreDayFour0.0870.0400.0990.0000.2310.0130.0380.1960.4580.4611.0000.2190.4140.2960.0690.0690.0950.0760.0600.6400.0890.1900.268
MensesScoreDayOne0.0870.0590.0510.0560.2180.0540.0930.1930.1770.1400.2191.0000.2170.1950.0830.0460.0940.0560.0360.1770.0610.1600.068
MensesScoreDayThree0.0680.0190.0360.0210.2150.0000.0000.1450.3570.3010.4140.2171.0000.3050.0920.0410.0580.0710.0680.5750.0900.1770.205
MensesScoreDayTwo0.0500.0210.0890.0810.1200.0560.0830.1120.3530.1630.2960.1950.3051.0000.0780.0270.0000.0980.0110.5110.0000.0810.089
NumberofDaysofIntercourse-0.0200.0710.1010.0280.1190.3200.1320.0160.0550.0480.0690.0830.0920.0781.0000.1170.0420.0220.0260.0090.0410.1060.043
ReproductiveCategory0.1390.0000.1980.1890.1190.1550.1800.3010.3000.0660.0690.0460.0410.0270.1171.0000.0000.1310.0280.2110.0760.1560.083
TotalDaysofFertility-0.0700.6180.362-0.2840.2400.1120.218-0.1900.1350.1530.0950.0940.0580.0000.0420.0001.0000.3480.0820.0960.8170.1760.125
TotalFertilityFormula-0.1790.4420.8380.4700.0980.2350.597-0.2470.1800.0880.0760.0560.0710.0980.0220.1310.3481.000-0.0450.1330.2250.0450.213
TotalHighPostPeak-0.0930.042-0.063-0.1260.2410.0420.0130.0770.0900.0570.0600.0360.0680.0110.0260.0280.082-0.0451.0000.0990.144-0.0270.137
TotalMensesScore-0.1400.0880.1700.0980.2820.0980.144-0.0440.7990.6510.6400.1770.5750.5110.0090.2110.0960.1330.0991.0000.0410.0430.376
TotalNumberofHighDays-0.0630.6220.200-0.2820.2170.0940.150-0.0580.0530.1060.0890.0610.0900.0000.0410.0760.8170.2250.1440.0411.000-0.1280.019
TotalNumberofPeakDays-0.0220.9560.244-0.0200.7440.1130.077-0.1530.0340.2200.1900.1600.1770.0810.1060.1560.1760.045-0.0270.043-0.1281.0000.259
UnusualBleeding0.0000.0000.2130.2370.0230.0000.1820.1470.4040.2560.2680.0680.2050.0890.0430.0830.1250.2130.1370.3760.0190.2591.000

Missing values

2024-12-11T15:51:42.984825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-11T15:51:43.706686image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-11T15:51:44.489448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CycleNumberGroupCycleWithPeakorNotReproductiveCategoryLengthofCycleEstimatedDayofOvulationLengthofLutealPhaseFirstDayofHighTotalNumberofHighDaysTotalHighPostPeakTotalNumberofPeakDaysTotalDaysofFertilityTotalFertilityFormulaLengthofMensesMensesScoreDayOneMensesScoreDayTwoMensesScoreDayThreeMensesScoreDayFourMensesScoreDayFiveTotalMensesScoreNumberofDaysofIntercourseIntercourseInFertileWindowUnusualBleeding
010102917121250291553321110510
120102715121320261353321110610
23010291514NaN10251353321110510
340102715121320261353332112300
450102816121240281453322111510
560102615111050291353321110410
67010291613NaN10251253321110600
78012241410950291043321NaN9500
890102816129702111361332212400
9100102817111340281453321110500
CycleNumberGroupCycleWithPeakorNotReproductiveCategoryLengthofCycleEstimatedDayofOvulationLengthofLutealPhaseFirstDayofHighTotalNumberofHighDaysTotalHighPostPeakTotalNumberofPeakDaysTotalDaysofFertilityTotalFertilityFormulaLengthofMensesMensesScoreDayOneMensesScoreDayTwoMensesScoreDayThreeMensesScoreDayFourMensesScoreDayFiveTotalMensesScoreNumberofDaysofIntercourseIntercourseInFertileWindowUnusualBleeding
1655211028181011504111662321110710
1656311029171211503101563322112710
16574110301911101002131772332113600
1658511030191113404101791331214511
165961103219131530381772322112910
1660711029191013503101582332215801
1661811028171112304913633211111110
16629110281612114039125322119700
166310111402713131301NaN2462332112300
16641111224NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

CycleNumberGroupCycleWithPeakorNotReproductiveCategoryLengthofCycleEstimatedDayofOvulationLengthofLutealPhaseFirstDayofHighTotalNumberofHighDaysTotalHighPostPeakTotalNumberofPeakDaysTotalDaysofFertilityTotalFertilityFormulaLengthofMensesMensesScoreDayOneMensesScoreDayTwoMensesScoreDayThreeMensesScoreDayFourMensesScoreDayFiveTotalMensesScoreNumberofDaysofIntercourseIntercourseInFertileWindowUnusualBleeding# duplicates
0101025111466029951222180002
11010281414NaN10251243211NaN70002
21010281810NaN10251641221NaN63102
3101029191014502917712322122102
42010231499502912713322130002
53010241212840281041221NaN63002
630102516910602101461222193102
73010271710NaN102515612332122002
84010251781250291551222184102
9500029NaNNaNNaN00002451222181102